Maps exist to convey information to people, whether that information is how to get from one point to another or how many oil fields are located in a given region. Effective cartography can convey that information efficiently to map users.In this course, you will be introduced to a five-step workflow for designing and creating maps. This workflow can be applied to any map or output medium (print or digital). This course will cover all steps of the workflow in general terms, emphasizing the first two steps: the cartographic planning process and data evaluation.After completing this course, you will be able to perform the following tasks:Identify and describe the cartographic workflow steps.Explain cartographic design controls and how they drive map creation.Apply the planning step of the cartographic workflow.Evaluate data sources to determine applicability.Discuss why basemap and operational layers are important.Assign the correct coordinate system to data based on the geographic extent and map objective.Assess the level of detail required for a map and apply generalization techniques when appropriate.

According to Cognitive Market Research, the global electronic cartography market size is USD 26.94 billion in 2024 and will expand at a compound annual growth rate (CAGR) of 9.49% from 2024 to 2031. Market Dynamics of Electronic Cartography Market

Key Drivers for Electronic Cartography Market

Rising use of Smartphones and IoT - The prominent factor that drives the market growth include the widespread use of smartphones, tablets, and electronic devices. In addition rise in the usage of Internet of Things (IoT) devices, heightened the demand for real-time mapping solutions, consequently driving the demand for the electronic cartography market. In addition, growing dependence on location-based services (LBS), Geographic Information Systems (GIS), and GPS applications for searching nearby theatre halls, gasoline stations, restaurants, urban planning, disaster management, is another factor that drives the demand for electronic cartography during the forecast period.
The increasing need for real-time data mapping to create precise and current digital representations, combined with the capability to analyze and visualize streaming data from sensors, devices, and social media feeds, is expected to propel market growth.

Key Restraints for Electronic Cartography Market

Integrating geographic,and geo-social data from different sources, such as social media and satellite imagery, can be challenging due to differences in data formats and scales.
Lack of expertise among users regarding the adoption of electronic cartography in marine industry may hampered the market growth

Introduction of the Electronic Cartography Market

Electronic cartography is a technology that allows to simulate the surrounding area with the help of special technical means and computer programs. Electronic cartography integrated with various processes such as data processing, data acquisitions, map distribution, and map creation. As the demand for topographical information systems grows, the deployment of digital mapping has grown in the government and public sectors. The Science & Technology Directorate (S&T), in May 2024,has launched a digital indoor map navigator Mappedin. This digital indoor map navigator transform floor plans into interactive and easily maintainable digitized maps, and is currently being used by both response agencies and corporate clients. Mappedin provides high-quality 3D map creation, easy-to-use mapping tools and data, map sharing, and data maintenance, to city executives, building owner operators and first responders to make and deliver maps for a variety of safety-related situations—from advance preparation and planning to assistance during emergency incidents. Additionally the rapid rise in the number of smartphone and internet users has fueled industry expansion. Additionally, the increasing number of connected and semi-autonomous vehicles along with anticipated advancements in self-driving and navigation technologies, are expected to boost the demand for electronic cartography market.

This 1m Digital Surface Model (DSM) shaded relief is derived from first-stop Light Detection and Ranging (LiDAR) point cloud data from September 2005 for the Green Lakes Valley, near Boulder Colorado. The DSM was created from LiDAR point cloud tiles subsampled to 1-meter postings, acquired by the National Center for Airborne Laser Mapping (NCALM) project. This data was collected in collaboration between the University of Colorado, Institute of Arctic and Alpine Research (INSTAAR) and NCALM, which is funded by the National Science Foundation (NSF). The DSM shaded relief has the functionality of a map layer for use in Geographic Information Systems (GIS) or remote sensing software. Total area imaged is 35 km^2. The LiDAR point cloud data was acquired with an Optech 1233 Airborne Laser Terrain Mapper (ALTM) and mounted in a twin engine Piper Chieftain (N931SA) with Inertial Measurement Unit (IMU) at a flying height of 600 m. Data from two GPS (Global Positioning System) ground stations were used for aircraft trajectory determination. The continuous DSM surface was created by mosaicing and then kriging 1 km2 LiDAR point cloud LAS-formated tiles using Golden Software's Surfer 8 Kriging algorithm. Horizontal accuracy and vertical accuracy is unknown. cm RMSE at 1 sigma. The layer is available in GEOTIF format approx. 265 MB of data. It has a UTM zone 13 projection, with a NAD83 horizonal datum and a NAVD88 vertical datum computed using NGS GEOID03 model, with FGDC-compliant metadata. This shaded relief model was also generated. A similar layer, the Digital Terrain Model (DTM), is a ground-surface elevation dataset better suited for derived layers such as slope angle, aspect, and contours. A processing report and readme file are included with this data release. The DSM dataset is available through an unrestricted public license. The LiDAR DEMs will be of interest to land managers, scientists, and others for study of topography, ecosystems, and environmental change. NOTE: This EML metadata file does not contain important geospatial data processing information. Before using any NWT LTER geospatial data read the arcgis metadata XML file in either ISO or FGDC compliant format, using ArcGIS software (ArcCatalog > description), or by viewing the .xml file provided with the geospatial dataset.

Scale reduction from source to target maps inevitably leads to conflicts of map symbols in cartography and geographic information systems (GIS). Displacement is one of the most important map generalization operators and it can be used to resolve the problems that arise from conflict among two or more map objects. In this paper, we propose a combined approach based on constraint Delaunay triangulation (CDT) skeleton and improved elastic beam algorithm for automated building displacement. In this approach, map data sets are first partitioned. Then the displacement operation is conducted in each partition as a cyclic and iterative process of conflict detection and resolution. In the iteration, the skeleton of the gap spaces is extracted using CDT. It then serves as an enhanced data model to detect conflicts and construct the proximity graph. Then, the proximity graph is adjusted using local grouping information. Under the action of forces derived from the detected conflicts, the proximity graph is deformed using the improved elastic beam algorithm. In this way, buildings are displaced to find an optimal compromise between related cartographic constraints. To validate this approach, two topographic map data sets (i.e., urban and suburban areas) were tested. The results were reasonable with respect to each constraint when the density of the map was not extremely high. In summary, the improvements include (1) an automated parameter-setting method for elastic beams, (2) explicit enforcement regarding the positional accuracy constraint, added by introducing drag forces, (3) preservation of local building groups through displacement over an adjusted proximity graph, and (4) an iterative strategy that is more likely to resolve the proximity conflicts than the one used in the existing elastic beam algorithm.

The Human Geography Map (World Edition) web map provides a detailed vector basemap with a monochromatic style and content adjusted to support Human Geography information. Where possible, the map content has been adjusted so that it observes WCAG contrast criteria.This basemap, included in the ArcGIS Living Atlas of the World, uses 3 vector tile layers:Human Geography Label, a label reference layer including cities and communities, countries, administrative units, and at larger scales street names.Human Geography Detail, a detail reference layer including administrative boundaries, roads and highways, and larger bodies of water. This layer is designed to be used with a high degree of transparency so that the detail does not compete with your information. It is set at approximately 50% in this web map, but can be adjusted.Human Geography Base, a simple basemap consisting of land areas in a very light gray only.The vector tile layers in this web map are built using the same data sources used for other Esri Vector Basemaps. For details on data sources contributed by the GIS community, view the map of Community Maps Basemap Contributors. Esri Vector Basemaps are updated monthly.Learn more about this basemap from the cartographic designer in Introducing a Human Geography Basemap.Use this MapThis map is designed to be used as a basemap for overlaying other layers of information or as a stand-alone reference map. You can add layers to this web map and save as your own map. If you like, you can add this web map to a custom basemap gallery for others in your organization to use in creating web maps. If you would like to add this map as a layer in other maps you are creating, you may use the tile layer item referenced in this map.

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

The Geofabric Surface Cartography product provides a set of related feature classes to be used as the basis for the production of consistent hydrological cartographic maps. This product contains a geometric representation of the (major) surface water features of Australia (excluding external territories). Primarily, these are natural surface hydrology features but the product also contains some man-made features (notably reservoirs, canals and other hydrographic features).

The product is fully topologically correct which means that all the stream segments flow in the correct direction.

This product contains fifteen feature types including: Waterbody, Mapped Stream, Mapped Node, Mapped Connectivity (Upstream), Mapped Connectivity (Downstream), Sea, Estuary, Dam, Structure, Canal Line, Water Pipeline, Terrain Break Line, Hydro Point, Hydro Line and Hydro Area.

Purpose

This product contains a geometric representation of the (major) surface water features of 'geographic Australia' excluding external territories. It is intended to be used as the basis for the production of consistent hydrological cartographic map products, as well as the visualisation of surface hydrology within a GIS to support the selection of features for inclusion in cartographic map production.

This product can also be used for stream tracing operations both upstream and downstream however, as this is a mapped representation, streams may be represented as interrupted or intermittent features. In contrast, the Geofabric Surface Network product represents the same stream as a continuous connected feature, that is, the path that stream would take (according to the terrain model) if sufficient water were available for flow. Therefore, for stream tracing operations where full stream connectivity is required, the Geofabric Surface Network product should be used.

Dataset History

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

Geofabric Surface Cartography is part of a suite of Geofabric products produced by the Australian Bureau of Meteorology. The source data input for the Geofabric Surface Cartography product is the AusHydro v1.7.2 (AusHydro) surface hydrology data set. The AusHydro database provides a seamless surface hydrology layer for Australia at a nominal scale of 1:250,000. It consists of lines, points and polygons representing natural and man-made features such as watercourses, lakes, dams and other water bodies. The natural watercourse layer consists of a linear network with a consistent topology of links and nodes that provide directional flow paths through the network for hydrological analysis.

This network was used to produce the GEODATA 9 Second Digital Elevation Model (DEM-9S) Version 3 of Australia (https://www.ga.gov.au/products/servlet/controller?event=GEOCAT_DETAILS&catno=66006).

Geofabric Surface Cartography is an amalgamation of two primary datasets. The first is the hydrographic component of the GEODATA TOPO 250K Series 3 (GEODATA 3) product released by Geoscience Australia (GA) in 2006. The GEODATA 3 dataset contains the following hydrographic features: canal lines, locks, rapid lines, spillways, waterfall points, bores, canal areas, flats, lakes, pondage areas, rapid areas, reservoirs, springs, watercourse areas, waterholes, water points, marine hazard areas, marine hazard points and foreshore flats.

It also provides information on naming, hierarchy and perenniality. The dataset also contains cultural and transport features that may intersect with hydrographic features. These include: railway tunnels, rail crossings, railway bridges, road tunnels, road bridges, road crossings, water pipelines.

Refer to the GEODATA 3 User Guide http://www.ga.gov.au/meta/ANZCW0703008969.html for additional information.

The second primary dataset is based on the GEODATA TOPO-250K Series 1 (GEODATA 1) watercourse lines completed by GA in 1994, which was supplemented by additional line work captured by the Australian National University (ANU) during the production of the DEM-9S to improve the representation of surface water flow. This natural watercourse dataset consists of directional flow paths and provides a direct link to the flow paths derived from the DEM. There are approximately 700,000 more line segments in this version of the data.

AusHydro uses the natural watercourse geometry from the ANU enhanced GEODATA 1 data, and the attributes (names, perenniality and hierarchy) associated with GEODATA 3 to produce a fully attributed data set with topologically correct flow paths. The attributes from GEODATA 3 were attached using spatial queries to identify common features between the two datasets. Additional semi-automated and manual editing was undertaken to ensure consistent attribution along the entire network.

AusHydro dataset includes a unique identifier for each line, point and polygon. AusHydro-ID will be used to maintain the dataset and to incorporate higher resolution datasets in the future. The AusHydro-ID will be linked to the ANUDEM streams through a common segment identifier and ultimately to a set of National Catchments Boundaries (NCBs).

Changes at v2.1

! New Water Storages in the WaterBody FC.

Changes at v2.1.1

! 16 New BoM Water Storages attributed in the AHGFWaterBody feature class

and 1 completely new water storage feature added.



- Correction to spelling of Numeralla river in AHGFMappedStream (formerly

Numaralla).



- Flow direction of Geometric Network set.

Processing steps:

1. AusHydro Surface Hydrology dataset is received and loaded into the Geofabric development GIS environment

2. feature classes from AusHydro are recomposed into composited Geofabric hydrography dataset feature classes in the Geofabric Maintenance Geodatabase.

3. re-composited feature classes in the Geofabric Maintenance Geodatabase Hydrography Dataset are assigned unique Hydro-IDs using ESRI ArcHydro for Surface Water (ArcHydro: 1.4.0.180 and ApFramework: 3.1.0.84)

4. feature classes from the Geofabric Maintenance Geodatabase hydrography dataset are extracted and reassigned to the Geofabric Surface Cartography Feature Dataset within the Geofabric Surface Cartography Geodatabase.

A complete set of data mappings, from input source data to Geofabric Products, is included in the Geofabric Product Guide, Appendices.

Dataset Citation

Bureau of Meteorology (2014) Geofabric Surface Cartography - V2.1.1. Bioregional Assessment Source Dataset. Viewed 12 December 2018, http://data.bioregionalassessments.gov.au/dataset/ce5b77bf-5a02-4cf8-9cf2-be4a2cee2677.

Did you know that George Washington was a cartographer? He was a surveyor and map maker in his early years, and continued to make his own maps for practical purposes throughout his life. Cool, right?George's StyleHere is a map he made of his farm, just dripping with hand-wrought charm:The ArcGIS Pro style available here is compiled of material textures and George's hand-drawn elements sampled from this very map. That means, when you use it, your map is wrought in the very hand of George Washington. What a time to be alive.Check out these examples that Ernst Eijkelenboom whipped up of his native Netherlands...Glorious.What You GetAre you ready to cartographicize like the first president of the United States? Here's what you'll find in the style...How to Install?Save this style file somewhere on your computer. Then, in Pro, open up the Catalog view, and expand the Style category. Right-click, and choose “Add.” Then just browse to where you saved George Washington. Pow! You’ll be whipping up maps that look like they were scribed by the right hand (I surmise, based on the way his trees lean) of George, himself.If you would like to make your own styles, based on the texture images I extracted from George’s map, then you can have at them here.Happy Presidential Throwback Mapping! John Nelson

Cartographer and GIS expert. Proven track of commercial experience. Since 2001, the leader of teams specializing in designing and maintaining spatial databases for navigation systems and modeling topographical data. Knowledge of Polish Spatial Data Infrastructure. Polish National Topographical Database model designer. Directly involved in the design and implementation of the Spatial Data Infrastructure in Poland. Vice-dean for Science and Development at the Faculty of Geodesy and Cartography at Warsaw University of Technology (2012-2016). Vice-Dean for Development and Cooperation with the Economy at the Faculty of Geodesy and Cartography at Warsaw University of Technology (2020-2024). Originator and project manager of the creation of the Center for Geospatial Analysis and Satellite Computing (CENAGIS). Advisor (expert) to the Head Office of Geodesy and Cartography in Poland (from 1999) in the SDI area. The initiator of the establishment of the Laboratory of Mobile Cartography and author of the teaching program in the field of Geoinformatics at Warsaw University of Technology. More than ten years of experience in managing the work of GIS department and GIS Database Operation Department (Director) in the capital group of PPWK/Mobile Internet Technology (joint-stock company) (among many tasks, several years of cooperation with Google Company - delivering of spatial dataset for the Polish territory). Membership of professional bodies (selected): • The Polish National Committe for International Cartographic Association (from 2004) • The Association of Polish Cartographers (from 1999, from 2013 Member of the Board) • The Geoinformatics Commision of the Polish Academy of Arts and Sciences (from 2016) • The Committee on Geodesy of the Polish Academy of Sciences, The Chair of Geoinformation Section (from 2016) • The Scientific Council of Polish Polar Consortium (2014-2022) • The Chairman of The Working Group "Smart networks and geoinformation technologies" (The Polish Smart Specialization) at the Ministry of Development (2015-2022) • V-Ce Chairman Of National Council For Spatial Information In Poland (From 2018) (inter-ministeral committee)

The Electronic Cartography market, valued at $17860 million in 2025, exhibits a steady growth trajectory with a CAGR of 0.8%. This relatively low CAGR suggests a mature market characterized by incremental innovation rather than explosive growth. Key drivers include the increasing demand for enhanced navigation systems across diverse sectors like maritime, aviation, and automotive. The integration of advanced technologies such as GPS, GIS, and sensor data enhances the accuracy and functionality of electronic charts, fueling market growth. However, the market faces challenges such as high initial investment costs for advanced systems and the need for regular software and data updates. Furthermore, competition among established players like Navionics, Honeywell, Thales, Jeppesen, Universal Avionics, Rockwell Collins, Transas Marine, Northrop Grumman, Garmin, and IIC Technologies is intense, necessitating continuous product development and cost optimization. Despite the relatively modest CAGR, specific segments within the Electronic Cartography market may experience higher growth rates. For example, the integration of electronic charts with autonomous navigation systems is a rapidly evolving area, potentially driving significant future market expansion. Furthermore, emerging markets in developing economies, coupled with the increasing demand for safer and more efficient transportation, could provide new opportunities for growth. The market's future success hinges on the ability of companies to develop innovative solutions addressing evolving user needs, particularly regarding data accuracy, real-time updates, and seamless integration with other onboard systems. A focus on cybersecurity aspects, crucial in protecting sensitive navigational data, will also be key to continued market stability and growth.

This 1m Digital Surface Model (DSM) is derived from first-stop Light Detection and Ranging (LiDAR) point cloud data from September 2005 for the Green Lakes Valley, near Boulder Colorado. The DSM was created from LiDAR point cloud tiles subsampled to 1-meter postings, acquired by the National Center for Airborne Laser Mapping (NCALM) project. This data was collected in collaboration between the University of Colorado, Institute of Arctic and Alpine Research (INSTAAR) and NCALM, which is funded by the National Science Foundation (NSF). The DSM has the functionality of a map layer for use in Geographic Information Systems (GIS) or remote sensing software. Total area imaged is 35 km^2. The LiDAR point cloud data was acquired with an Optech 1233 Airborne Laser Terrain Mapper (ALTM) and mounted in a twin engine Piper Chieftain (N931SA) with Inertial Measurement Unit (IMU) at a flying height of 600 m. Data from two GPS (Global Positioning System) ground stations were used for aircraft trajectory determination. The continuous DSM surface was created by mosaicing and then kriging 1 km2 LiDAR point cloud LAS-formated tiles using Golden Software's Surfer 8 Kriging algorithm. Horizontal accuracy and vertical accuracy is unknown. cm RMSE at 1 sigma. The layer is available in GEOTIF format approx. 265 MB of data. It has a UTM zone 13 projection, with a NAD83 horizonal datum and a NAVD88 vertical datum computed using NGS GEOID03 model, with FGDC-compliant metadata. A shaded relief model was also generated. A similar layer, the Digital Terrain Model (DTM), is a ground-surface elevation dataset better suited for derived layers such as slope angle, aspect, and contours. A processing report and readme file are included with this data release. The DSM is available through an unrestricted public license. The LiDAR DEMs will be of interest to land managers, scientists, and others for study of topography, ecosystems, and environmental change. NOTE: This EML metadata file does not contain important geospatial data processing information. Before using any NWT LTER geospatial data read the arcgis metadata XML file in either ISO or FGDC compliant format, using ArcGIS software (ArcCatalog > description), or by viewing the .xml file provided with the geospatial dataset.

These data were created for mapping usage, areas have been trimmed to the high water mark of Great Salt Lake and Utah Lake. Creates a clearer visual picture of the municipalities.

Current thru June 30, 2017

This dataset contains useful vector maps to work with with Goode's Homolosine projection. The list of files included are:

• CounterDomain.geojson - a polygonal approximation of the Homolosine projection counter-domain. This can be used to fix vectors wrongly projected by programmes that consider the counter-domain to be infinite. It can also be used to represent the seas in global mapping.
• ParallelsMeridians.geojson - a set of meridians and parallels to be used in the creation of global maps.
• Homolosine.crs - the PROJ string defining the Homolosine projection (referenced by the GeoJSON slides)
• LICENCE - full text of the licence (EUPL-1.2)

These datasets were generated with the open souce programme homolosine-vectors, available at: https://gitlab.com/ldesousa/homolosine-vectors

A geospatial image-based eye movement dataset called GeoEye, is a publicly shared, widely available eye movement dataset. This dataset consists of 110 college-aged participants who freely viewed 500 images, including thematic maps, remote sensing images, and street view images.

Continuing the tradition of the best-selling Getting to Know series, Getting to Know ArcGIS Pro 2.6 teaches new and existing GIS users how to get started solving problems using ArcGIS Pro. Using ArcGIS Pro for these tasks allows you to understand complex data with the leading GIS software that many businesses and organizations use every day.Getting to Know ArcGIS Pro 2.6 introduces the basic tools and capabilities of ArcGIS Pro through practical project workflows that demonstrate best practices for productivity. Explore spatial relationships, building a geodatabase, 3D GIS, project presentation, and more. Learn how to navigate ArcGIS Pro and ArcGIS Online by visualizing, querying, creating, editing, analyzing, and presenting geospatial data in both 2D and 3D environments. Using figures to show each step, Getting to Know ArcGIS Pro 2.6 demystifies complicated process like developing a geoprocessing model, using Python to write a script tool, and the creation of space-time cubes. Cartographic techniques for both web and physical maps are included.Each chapter begins with a prompt using a real-world scenario in a different industry to help you explore how ArcGIS Pro can be applied for operational efficiency, analysis, and problem solving. A summary and glossary terms at the end of every chapter help reinforce the lessons and skills learned.Ideal for students, self-learners, and seasoned professionals looking to learn a new GIS product, Getting to Know ArcGIS Pro 2.6 is a broad textbook and desk reference designed to leave users feeling confident in using ArcGIS Pro on their own.AUDIENCEProfessional and scholarly. Higher education.AUTHOR BIOMichael Law is a cartographer and GIS professional with more than a decade of experience. He was a cartographer for Esri, where he developed cartography for books, edited and tested GIS workbooks, and was the editor of the Esri Map Book. He continues to work with GIS software, writing technical documentation, teaching training courses, and designing and optimizing user interfaces.Amy Collins is a writer and editor who has worked with GIS for over 16 years. She was a technical editor for Esri, where she honed her GIS skills and cultivated an interest in designing effective instructional materials. She continues to develop books on GIS education, among other projects.Pub Date: Print: 10/6/2020 Digital: 8/18/2020 ISBN: Print: 9781589486355 Digital: 9781589486362 Price: Print: $84.99 USD Digital: $84.99 USD Pages: 420 Trim: 7.5 x 9.25 in.Table of ContentsPrefaceChapter 1 Introducing GISExercise 1a: Explore ArcGIS OnlineChapter 2 A first look at ArcGIS Pro Exercise 2a: Learn some basics Exercise 2b: Go beyond the basics Exercise 2c: Experience 3D GISChapter 3 Exploring geospatial relationshipsExercise 3a: Extract part of a dataset Exercise 3b: Incorporate tabular data Exercise 3c: Calculate data statistics Exercise 3d: Connect spatial datasetsChapter 4 Creating and editing spatial data Exercise 4a: Build a geodatabase Exercise 4b: Create features Exercise 4c: Modify featuresChapter 5 Facilitating workflows Exercise 5a: Manage a repeatable workflow using tasks Exercise 5b: Create a geoprocessing model Exercise 5c: Run a Python command and script toolChapter 6 Collaborative mapping Exercise 6a: Prepare a database for data collection Exercise 6b: Prepare a map for data collection Exercise 6c: Collect data using ArcGIS CollectorChapter 7 Geoenabling your projectExercise 7a: Prepare project data Exercise 7b: Geocode location data Exercise 7c: Use geoprocessing tools to analyze vector dataChapter 8 Analyzing spatial and temporal patternsExercise 8a: Create a kernel density map Exercise 8b: Perform a hot spot analysis Exercise 8c: Explore the results in 3D Exercise 8d: Animate the dataChapter 9 Determining suitability Exercise 9a: Prepare project data Exercise 9b: Derive new surfaces Exercise 9c: Create a weighted suitability modelChapter 10 Presenting your project Exercise 10a: Apply detailed symbology Exercise 10b: Label features Exercise 10c: Create a page layout Exercise 10d: Share your projectAppendix Image and data source credits Data license agreement GlossaryGetting to Know ArcGIS Pro 2.6 | Official Trailer | 2020-08-10 | 00:57

Digital line graph (DLG) data are digital representations of cartographic information. DLGs of map features are converted to digital form from maps and related sources. Intermediate-scale DLG data are derived from USGS 1:100,000-scale 30- by 60-minute quadrangle maps. If these maps are not available, Bureau of Land Management planimetric maps at a scale of 1:100,000 are used. Intermediate-scale DLGs are sold in five categories: (1) Public Land Survey System; (2) boundaries; (3) transportation; (4) hydrography; and (5) hypsography. All DLG data distributed by the USGS are DLG-Level 3 (DLG-3), which means the data contain a full range of attribute codes, have full topological structuring, and have passed certain quality-control checks.

Produced from NaturalEarth 1:50m Admin0 - Details map sub units cultural vectordata (version 5.1.1) and with Vectors for Goode's Homolosine projection

Created with QGIS 3.20.3

Connectivity models are useful tools that improve the ability of researchers and managers to plan land use for conservation and preservation. Most connectivity models function in a point-to-point or patch-to-patch fashion, limiting their use for assessing connectivity over very large areas. In large or highly fragmented systems, there may be so many habitat patches of interest that assessing connectivity among all possible combinations is prohibitive. To overcome these conceptual and practical limitations, we hypothesized that minor adaptation of the Circuitscape model can allow the creation of omnidirectional connectivity maps illustrating flow paths and variations in the ease of travel across a large study area. We tested this hypothesis in a 24,300 km2 study area centered on the Montérégie region near Montréal, Québec. We executed the circuit model in overlapping tiles covering the study region. Current was passed across the surface of each tile in orthogonal directions, and then the tiles were reassembled to create directional and omnidirectional maps of connectivity. The resulting mosaics provide a continuous view of connectivity in the entire study area at the full original resolution. We quantified differences between mosaics created using different tile and buffer sizes and developed a measure of the prominence of seams in mosaics formed with this approach. The mosaics clearly show variations in current flow driven by subtle aspects of landscape composition and configuration. Shown prominently in mosaics are pinch points, narrow corridors where organisms appear to be required to traverse when moving through the landscape. Using modest computational resources, these continuous, fine-scale maps of nearly unlimited size allow the identification of movement paths and barriers that affect connectivity. This effort develops a powerful new application of circuit models by pinpointing areas of importance for conservation, broadening the potential for addressing intriguing questions about resource use, animal distribution, and movement.

MLU is a geographic database focused on urban land use for the Community of Madrid, including all the municipalities of the Community of Madrid.

Natural Earth is a public domain map dataset available at 1:10m, 1:50m, and 1:110 million scales (1:10m version is stored in the EEA-SDI). Featuring tightly integrated vector and raster data, with Natural Earth one can make a variety of visually pleasing, well-crafted maps with cartography or GIS software. Natural Earth was built through a collaboration of many volunteers and is supported by NACIS (North American Cartographic Information Society), and is free for use in any type of project. The carefully generalized linework maintains consistent, recognizable geographic shapes at 1:10m, 1:50m, and 1:110m scales. Natural Earth was built from the ground up in order for all data layers align precisely with one another. For example, where rivers and country borders are one and the same, the lines are coincident. Most data contain embedded feature names, which are ranked by relative importance. Other attributes facilitate faster map production, such as width attributes assigned to river segments for creating tapers.

Cultural vector data themes: + Countries + Disputed areas and breakaway regions + First order admin + Populated places + Urban polygons + Parks and protected areas

             + Pacific nation groupings

• Water boundary indicators

Physical vector data themes: + Coastline + Land

             + Ocean

• Minor islands
• Reefs
• Physical region features
• Rivers and lake centerlines
• Lakes + Glaciated areas
• Antarctic ice shelves
• Bathymetry
• Geographic lines
• Graticules

A list of all countries with corresponding group IDs at the second level and third level of CONCOR. (PDF)